Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries : 7th International Workshop, BrainLes 2021, Held in Conjunction with MICCAI 2021, Virtual Event, September 27, 2021, Revised Selected Papers, Part II / / edited by Alessandro Crimi, Spyridon Bakas

• Pubblicazione: Cham : , : Springer International Publishing : , : Imprint : Springer, , 2022
• Edizione: 1st ed. 2022.
• Descrizione fisica: 1 online resource (617 pages)
• Disciplina: 616.99481; 616.80754
• Soggetto topico: Computer vision; Medical informatics; Social sciences - Data processing; Application software; Education - Data processing; Artificial intelligence; Computer Vision; Health Informatics; Computer Application in Social and Behavioral Sciences; Computer and Information Systems Applications; Computers and Education; Artificial Intelligence
• Persona (resp. second.): BakasSpyridon; CrimiAlessandro
• Nota di bibliografia: Includes bibliographical references and index.
• Nota di contenuto: BiTr-Unet: a CNN-Transformer Combined Network for MRI Brain Tumor Segmentation -- Optimized U-Net for Brain Tumor Segmentation -- MS UNet: Multi-Scale 3D UNet for Brain Tumor Segmentation -- Evaluating Scale Attention Network for Automatic Brain Tumor Segmentation with Large Multi-parametric MRI Database -- Orthogonal-Nets: A large ensemble of 2D neural networks for 3D Brain Tumor Segmentation -- Feature Learning by Attention and Ensemble with 3D U-Net to Glioma Tumor Segmentation -- MRI Brain Tumor Segmentation Using Deep Encoder-Decoder Convolutional Neural Networks -- Brain Tumor Segmentation with Patch-based 3D Attention UNet from Multi-parametric MRI -- Dice Focal Loss with ResNet-like Encoder-Decoder architecture in 3D Brain Tumor Segmentation -- HNF-Netv2 for Brain Tumor Segmentation using multi-modal MR Imaging -- Disparity Autoencoders for Multi-class Brain Tumor Segmentation -- Disparity Autoencoders for Multi-class Brain Tumor Segmentation -- Disparity Autoencoders for Multi-class BrainTumor Segmentation -- Brain Tumor Segmentation in Multi-parametric Magnetic Resonance Imaging using Model Ensembling and Super-resolution -- Quality-aware Model Ensemble for Brain Tumor Segmentation -- Redundancy Reduction in Semantic Segmentation of 3D Brain Tumor MRIs -- An Ensemble Approach to Automatic Brain Tumor Segmentation -- Extending nn-UNet for brain tumor segmentation -- Generalized Wasserstein Dice Loss, Test-time Augmentation, and Transformers for the BraTS 2021 challenge -- Coupling nnU-Nets with Expert Knowledge for Accurate Brain Tumor Segmentation from MRI -- Deep Learning based Ensemble Approach for 3D MRI Brain Tumor Segmentation -- Prediction of MGMT Methylation Status of Glioblastoma using Radiomics and Latent Space Shape Features -- bining CNNs With Transformer for Multimodal 3D MRI Brain Tumor Segmentation -- Automatic Brain Tumor Segmentation with a Bridge-Unet deeply supervised enhanced with downsampling pooling combination, Atrous Spatial Pyramid Pooling, Squeeze-and-Excitation and EvoNorm.
• Sommario/riassunto: This two-volume set LNCS 12962 and 12963 constitutes the thoroughly refereed proceedings of the 7th International MICCAI Brainlesion Workshop, BrainLes 2021, as well as the RSNA-ASNR-MICCAI Brain Tumor Segmentation (BraTS) Challenge, the Federated Tumor Segmentation (FeTS) Challenge, the Cross-Modality Domain Adaptation (CrossMoDA) Challenge, and the challenge on Quantification of Uncertainties in Biomedical Image Quantification (QUBIQ). These were held jointly at the 23rd Medical Image Computing for Computer Assisted Intervention Conference, MICCAI 2020, in September 2021. The 91 revised papers presented in these volumes were selected form 151 submissions. Due to COVID-19 pandemic the conference was held virtually.
• Titolo autorizzato: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries
• ISBN: 3-031-09002-0
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Lingua di pubblicazione: Inglese
• Record Nr.: 9910584480403321
• Lo trovi qui: Univ. Federico II
• Serie: Lecture Notes in Computer Science, . 1611-3349 ; ; 12963